There are two main differences between our proposed MM-RealSR and existing modulation-based methods. First, existing methods are limited to be trained with simple degradations and known degradation levels. They cannot address the modulation problem for real-world images with complex and unknown degradations.
Second, existing methods (e.g., the recent CResMD [1]} and CUGAN [2]}) must be manually provided with degradation parameters for adjustment, which is over-complicated in processing abundant images.
In this paper, we also propose an unsupervised estimation degradation module to automatically estimate a most suitable degradation level. The users are then able to modulate the restoration strength based on the estimated degradation levels
Equipped with the unsupervised degradation estimation module (UDEM), our MM-RealSR can flexibly switch between modulation and non-modulation modes for restoration.
